Third-Order Nonlinear Optical Properties of Ge-Ga-S Chalcogenide Glasses

We have prepared GexGa4S96-x glasses for x=22.5, 27, 30 and 33.3 and GexGa8S92-x for x=32, 36 and measured their linear and nonlinear optical properties. The glasses exhibit broad transmission at a wavelength range from 1 to 12 μm. The evolution of linear, nonlinear index and two-photon absorption as a function of the content of Ge, and the relationship of n2 and β with linear refractive index and optical bandgap are analyzed. While the evolution of n2 and β is closer to the prediction by Sheik-Bahae et.al for optical nonlinearity of semiconductors. Eg of Ge-Ga-S is found to vary from 2.33 to 2.99eV, and the largest nonlinear index is 1.16×10-14cm2/W at composition of Ge32Ga8S60 .

INVENTARISASI TUMBUHAN BAWAH DI AREAL PROGRAM STUD

The understorey functions as a ground cover that maintains moisture so that the rapid decompositionprocess can provide nutrients for the main crop the nutrient cycle can take place perfectly, theavalanches that fall as litter will be returned to the tree in the form of nutrients which, as is known, willbe broken down by bacteria and microbes. This study aims to determine the type, number, andpresence of each understorey in the area of the Samarinda State Agricultural Polytechnic ForestManagement Study Program, precisely behind the Silviculture Laboratory. The research method useda single plot measuring 60 m x 32 m with sub plots measuring 2 m x 2 m which were arrangedsystematically so that there were 480 sub plots.The results of the study found as many as 23 speciesof understorey which were included in 22 genera, 18 families, as many as 14.616 individuals, in1.528 attendances. The most common species found were Asystasia intrusa as many as 5.728individuals with distribution in 304 plots, then Clidemia hirta (L.) D.Don with 1.864 plants withdistribution in 240 plots and Stenochlaena palutris (Burm.f.) Bedd species. as many as 1.344 with adistribution of 56 plots. While the types of Alpina sp and Imperata chylindrica as many as 8 plant in 8plot.

Design of a Computer-Aided Location Expert System Based on a Mathematical Approach

This article discusses how to calculate the location of a point on a surface using a mathematical approach on two levels. The first level uses the traditional calculation procedure via Cooper’s iterative method through a spreadsheet editor and a classic result display map. The second level uses the author-created computer-aided location expert system on the principle of calculation using Cooper’s iterative method with the direct graphical display of results. The problem is related to designing a practical computer location expert system, which is based on a new idea of using the resolution of a computer map as an image to calculate location. The calculated results are validated by comparing them with each other, and the defined accuracy for a particular example was achieved at the 32nd iteration with the position optima DC[x(32);y(32)] = [288.8;82.7], with identical results. The location solution in the case study to the defined accuracy was achieved at the 6th iteration with the position optima DC[x(6);y(6)] = [274;220]. The calculations show that the expert system created achieves the required parameters and is a handy tool for determining the location of a point on a surface.

An image analysis method for determining boiling front in minichannel heat exchanger

In this work a numerical method for determining boiling front in short time period of flow was presented. A non-stationary boiling in rectangular eleven minichannels (0.25 mm x 0.25 mm x 32 mm) was recorded using Phantom v1610 high speed camera with the speed of 6000 fps. In the algorithm correlation between subsequent frames was computed. Frames were divided into reference and test frames. In each iteration a part of a reference frame called ‘reference gate’ and moving part of a test frame called ‘moving gate’ were considered. A two-dimensional correlation coefficient was calculated. Such method allowed to identify the location of boiling front in each minichannel separately.

Pastoral Practices, Bedding and Fodder During the Early Neolithic Through Micromorphology at Cova Colomera (Southeastern Pre-Pyrenees, Iberia)

The microstratigraphic study of the Cova Colomera (Sant Esteve de la Sarga, Lleida, Spain) confirms that there are several discontinuous ovicaprid stabling episodes in the Late Cardial Neolithic sequence (c. 5250–4780 cal BC). There are episodes with and without combustion traces. From the burnt episodes, it has been possible to identify bedding and fodder due to their good preservation and abundance in the X-32 sector, specifically the level CE14. The main constituents are grassy remains and to a lesser extent, conifer twigs and needles, beech twigs, and box leaves. These data give an idea about the landscape near the cavity. From the nonburnt episodes, we emphasize the sector W-31, specifically the top of level CE13, in which bedding and fodder appear in a smaller quantity. Its components are also well preserved, with an emphasis on sheep/goat excrements in which it has been possible to identify part of their diet composed of leaves and culms of grasses (Poaceae). From these episodes and their components, we propose that Cova Colomera had different uses as a pen of a small size herd. In some episodes, the herd was more permanent in the cave, and therefore, more waste was generated, so burning was required; and in other episodes, occupation was more sporadic and the burning of waste was not so necessary. In short, Cova Colomera allows us to propose that the study of pastoral activities in caves and rockshelters is more complex than previous studies have shown and that it is necessary to analyze these records with high-resolution techniques to broaden the knowledge of these first livestock communities.

Restoration of non-structural damaged murals in Shenzhen Bao’an based on a generator–discriminator network

Shenzhen is a modern metropolis, but it hides a variety of valuable cultural heritage, such as ancient murals. How to effectively preserve and repair the murals is a worthy of discussion question. Here, we propose a generation-discriminator network model based on artificial intelligence algorithms to perform digital image restoration of ancient damaged murals. In adversarial learning, this study optimizes the discriminative network model. First, the real mural images and damaged images are spliced together as input to the discriminator network. The network uses a 5-layer encoder unit to down-sample the 1024 x 1024 x 3 image to 32 x 32 x 256. Then, we connect a layer of ZeroPadding2D to expand the image to 34 x 34 x 256, and pass the Conv2D layer, down-sample to 31 x 31 x 256, perform batch normalization, and repeat the above steps to get a 30 x 30 x 1 matrix. Finally, this part of the loss is emphasized in the loss function as needed to improve the texture detail information of the image generated by the Generator. The experimental results show that compared with the traditional algorithm, the PSNR value of the algorithm proposed in this paper can be increased by 5.86 db at most. The SSIM value increased by 0.13. Judging from subjective vision. The proposed algorithm can effectively repair damaged murals with dot-like damage and complex texture structures. The algorithm we proposed may be helpful for the digital restoration of ancient murals, and may also provide reference for mural restoration workers.

The Impact of Seismic Interpretation Methods on the Analysis of Faults: A Case Study from the Snøhvit Field, Barents Sea

Five seismic interpretation experiments were conducted on an area of interest containing a fault relay in the Snøhvit field, Barents Sea, Norway, to understand how interpretation method impacts the analysis of fault and horizon morphologies, fault lengths, and vertical displacement (throw). The resulting horizon and fault interpretations from the least and most successful interpretation methods were further analysed to understand the impact of interpretation method on geological modelling and hydrocarbon volume calculation. Generally, the least dense manual interpretation method of horizons (32 inlines (ILs) x 32 crosslines (XLs), 400 m) and faults (32 ILs, 400 m) resulted in inaccurate fault and horizon interpretations and underdeveloped relay morphologies and throw that can be considered inadequate for any detailed geological analysis. The densest fault interpretations (4 ILs, 50 m) and auto-tracked horizons (1 IL x 1 XL, 12.5 m) provided the most detailed interpretations, most developed relay and fault morphologies and geologically realistic throw distributions. Analysis of the geological modelling proved that sparse interpretation grids generate significant issues in the model itself which make it geologically inaccurate and lead to misunderstanding of the structural evolution of the relay. Despite significant differences between the two models the calculated in-place petroleum reserves are broadly similar in the least and most dense experiments. However, when considered at field-scale the magnitude of the differences in volumes that are generated solely by the contrasting interpretation methodologies clearly demonstrates the importance of applying accurate interpretation strategies.

Optimal Length of R-R Interval Segment Window for Lorenz Plot Detection of Paroxysmal Atrial Fibrillation by Machine Learning

Background: Heartbeat interval Lorenz plot (LP) imaging is a promising method for detecting atrial fibrillation (AF) in long-term monitoring, but the optimal segment window length for the LP images is unknown. We examined the performance of AF detection by LP images with different segment window lengths by machine learning with convolutional neural network (CNN). LP images with a 32 x 32-pixel resolution of non-overlapping segments with lengths between 10 and 500 beats were created from R-R intervals of 24-h ECG in 52 patients with chronic AF and 58 non-AF controls as training data and in 53 patients with paroxysmal AF and 52 non-AF controls as test data. For each segment window length, discriminant models were made by 5-fold cross-validation subsets of the training data and its classification performance was examined with the test data.Results: In machine learning with the training data, the averages of cross-validation scores were 0.995 and 0.999 for 10 and 20-beat LP images, respectively, and >0.999 for 50 to 500-beat images. The classification of test data showed good performance for all segment window lengths with an accuracy from 0.970 to 0.988. Positive likelihood ratio for detecting AF segments, however, showed a convex parabolic curve linear relationship to log segment window length and peaked at 85 beats, while negative likelihood ratio showed monotonous increase with increasing segment window length.Conclusions: This study suggests that the optimal segment window length that maximizes the positive likelihood ratio for detecting paroxysmal AF with 32 x 32-pixel LP image is 85 beats.